Abstract
We study the problem of domain adaptation for neural abstractive summarization. We make initial efforts in investigating what information can be transferred to a new domain. Experimental results on news stories and opinion articles indicate that neural summarization model benefits from pre-training based on extractive summaries. We also find that the combination of in-domain and out-of-domain setup yields better summaries when in-domain data is insufficient. Further analysis shows that, the model is capable to select salient content even trained on out-of-domain data, but requires in-domain data to capture the style for a target domain.- Anthology ID:
- W17-4513
- Volume:
- Proceedings of the Workshop on New Frontiers in Summarization
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Lu Wang, Jackie Chi Kit Cheung, Giuseppe Carenini, Fei Liu
- Venue:
- WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 100–106
- Language:
- URL:
- https://aclanthology.org/W17-4513
- DOI:
- 10.18653/v1/W17-4513
- Cite (ACL):
- Xinyu Hua and Lu Wang. 2017. A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization. In Proceedings of the Workshop on New Frontiers in Summarization, pages 100–106, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization (Hua & Wang, 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/W17-4513.pdf